There are two main types of background events: the generic \(B \overline{B}\)  spherical events and the jetlike \(q\overline{q}\)  continuum events.
The dominant background in the selection region is from the \(B \rightarrow J/\psi X\)  inclusive decays.
Other types of \(B \overline{B}\)  and continuum backgrounds also contribute.
Since the signal and background shapes are different, we use a multivariate analyzer based on the neural network package named NeuroBayes [1]} to distinguish the signal and background. We train the neural network using the signal MC and \(B \rightarrow J/\psi X\)  MC samples, with the following 33 input variables:
(1) 25 modified Fox-Wolfram moments treating the information of particles involved in the signal \(B\)  candidate separately from those in the rest of the event [2]},
(2) the cosine of the angle between the \(B\)  candidate and the beam axis,
(3) the angle between the thrust axis of the decay particles of the \(B\)  candidate and that of the remaining particles in the event,
(4) the event sphericity [3]},
(5) the missing mass, momentum and energy in the event,
(6) the sum of the transverse energy of the event, and
(7) the flavor tagging information [4]}.
Variables (1)–(6) are calculated in the \(\Upsilon (4S)\)  rest frame.
NeuroBayes returns an output in the range \(-1\)  to +1, where values closer to +1 are signallike and values closer to \(-1\)  are backgroundlike. The applied selection on the NeuroBayes output is determined by optimizing a figure of merit (FOM) defined as
\({\rm FOM} = \frac{\text{efficiency}}{0.5n+\sqrt{N_\text{bkg}}},\) 
